Update app.py

app.py

@@ -3,7 +3,14 @@ import pandas as pd
 import numpy as np
 import plotly.graph_objects as go
 import streamlit as st
+import altair as alt
+import matplotlib.pyplot as plt
+import seaborn as sns
+from bs4 import BeautifulSoup
+import requests
 from datetime import datetime
+from rich import print
+import jsonschema
 
 # Step 1: Define a function to fetch real-time market data up to the current date
 def fetch_data(ticker_symbol):
@@ -31,8 +38,8 @@ def calculate_indicators(df, lengthEMA=3, lengthRSI=14, momentumLength=3, trendL
     # Calculate Momentum
     df['Momentum'] = df['Close'].diff(momentumLength)
 
-    # Composite SIGNAL Calculation with Adjusted Weight
-    df['SignalComposite'] = (0.6 * df['SignalEMA']) + (0.2 * (df['RSI'] - 50) / 100) + (0.2 * (df['Momentum'] / df['Close'].rolling(window=lengthRSI).mean()))
+    # Composite SIGNAL Calculation
+    df['SignalComposite'] = (0.5 * df['SignalEMA']) + (0.3 * (df['RSI'] - 50) / 100) + (0.2 * (df['Momentum'] / df['Close'].rolling(window=lengthRSI).mean()))
 
     # Smooth the Composite SIGNAL with EMA
     df['SmoothedSignal'] = df['SignalComposite'].ewm(span=lengthEMA, adjust=False).mean()
@@ -40,20 +47,16 @@ def calculate_indicators(df, lengthEMA=3, lengthRSI=14, momentumLength=3, trendL
     # Trend Filter (SMA)
     df['TrendSMA'] = df['Close'].rolling(window=trendLength).mean()
 
-    # Adjusted Thresholds for Buy and Sell Signals
-    buyThreshold = 1.0
-    sellThreshold = -1.0
-    df['BuySignal'] = (df['SmoothedSignal'] > buyThreshold) & (df['Close'] > df['TrendSMA']) & (df['Close'] > df['MA50'])
-    df['SellSignal'] = (df['SmoothedSignal'] < sellThreshold) & (df['Close'] < df['TrendSMA']) & (df['Close'] < df['MA50'])
-
-    # Add a Cooldown Period for Signals
-    df['BuySignal'] = df['BuySignal'] & ~df['BuySignal'].shift(1).fillna(False)
-    df['SellSignal'] = df['SellSignal'] & ~df['SellSignal'].shift(1).fillna(False)
+    # Buy and Sell Signals
+    buyThreshold = 0.75
+    sellThreshold = -0.75
+    df['BuySignal'] = (df['SmoothedSignal'] > buyThreshold) & (df['Close'] > df['TrendSMA'])
+    df['SellSignal'] = (df['SmoothedSignal'] < sellThreshold) & (df['Close'] < df['TrendSMA'])
 
     return df
 
 # Step 3: Streamlit UI Setup for Stock Selection
-st.title("Simplified Indian Share Market Analysis")
+st.title("Advanced Indian Share Market Analysis")
 st.sidebar.header("Select Stock Ticker")
 
 # Add a stock selector input box
@@ -121,5 +124,33 @@ try:
 
     st.plotly_chart(fig)
 
+    # Step 7: Altair Chart for Moving Averages
+    st.subheader(f"Moving Averages for {ticker_symbol}")
+    alt_chart = alt.Chart(nifty_data.reset_index()).mark_line().encode(
+        x='Date:T',
+        y=alt.Y('MA20:Q', title='Moving Average (20-day)'),
+        color=alt.value('orange')
+    ).properties(title="20-Day Moving Average")
+    st.altair_chart(alt_chart, use_container_width=True)
+
+    # Step 8: Market News Integration Using BeautifulSoup
+    st.sidebar.header("Latest Market News")
+    news_url = 'https://www.moneycontrol.com/news/'
+    response = requests.get(news_url)
+    soup = BeautifulSoup(response.content, 'html.parser')
+
+    headlines = [headline.text for headline in soup.find_all('h2')[:5]]
+    st.sidebar.subheader("Top 5 News Headlines")
+    for idx, headline in enumerate(headlines, 1):
+        st.sidebar.write(f"{idx}. {headline}")
+
+    # Step 9: Alerts Using Rich Library
+    st.sidebar.subheader("Set Alerts:")
+    alert_type = st.sidebar.selectbox('Alert Type', ['Price', 'RSI'])
+    alert_value = st.sidebar.number_input('Enter Alert Value')
+
+    if alert_value:
+        print(f"[bold cyan]Alert Set for {alert_type} at Value:[/bold cyan] {alert_value}")
+
 except Exception as e:
     st.error(f"An error occurred: {str(e)}")